We employed two metrics, Recall@N and Normalized Discounted Cumulative Gain (NDCG), for evaluating the accuracy of the top-N recommendation [1]}, [2]}, [3]}. Recall@\(N\)  checks the number of test items included in the top-N list. The score is defined as \(\frac{\sum _{i=1}^N{rel_i}}{k}\) , where \(i\)  is the position in the list, \(k\)  is the number of test items and \(rel_i \in \lbrace 0, 1\rbrace \)  indicates whether the \(i\) -th item is relevant to the user or not. NDCG@\(N\)  takes into account the order of retrieved items in the list. DCG@\(N\)  is defined as \(\sum _{i=1}^N \frac{2^{rel_i} - 1}{log_2 (i+1)}\) . NDCG is the ratio of DCG to the maximum possible DCG for that user, which occurs when the recommended items are presented in decreasing order of user preference. We chose \(N = \lbrace 50, 100\rbrace \)  for the top-N recommendation.
